In an attempt to rebut economist Ed Dolan’s support of a carbon tax, I came across a RAND Study done for the Sacramento Municipal Utility District, which estimasted the short term elasticity of residential electricity demand at -0.2 and the long run elasticity of demand a -0.32.

This is a very inelastic market ( |elasticity| << 1 ), and so supports my argument that regulation is likely to be the most economically efficient approach to reducing residential electricity use.

Dolan compiled some numbers that put long run elasticity of gasoline demand at around 0.5, which also implies that regulation has a role to play in reducing gas usage, although it’s high enough that carbon taxes are also likely to be somewhat effective; a combination seems the best approach to me.

Equities… have low prices (and high expected returns) because their cash flows are discounted by society at high rates. The reason has to do with the anti-insurance aspect of equities: Their cash flows are highest in good states of nature whereby the value placed on the cash flows is low. In contrast, efforts to mitigate climate change by pricing carbon emissions will be most valuable to society if climate change turns out to have catastrophic consequences for society’s well-being. Because of this insurance aspect, society should be willing to pay higher prices for climate change mitigation.

FAJ Executive Editor Robert Litterman goes on to explain the mechanics behind carbon pricing models and their flaws, as well as why equity analysts are uniquely qualified to do these assessments.

I’ve long thought that financial market theory is uniquely applicable to understanding climate and the measures needed to mitigate climate change. what I don’t understand is why I hear so few analysts talking about it, so it was very refreshing to come across this article applying a deep understanding of economic pricing theory to what the greatest challenge the world will confront in the 21st century.

My study of chaos theory led to my conviction that knowing the limits of our ability to predict is much more important than the predictions themselves, a lesson I apply to both climate science and the financial markets.

Despite having written about financial markets and clean energy stocks regularly since 2006, I have never before explained in print what I meant by that. This summer’s heat wave and stock market turbulence illustrate how my intuition about chaos theory informs both my understanding of the climate and the stock market.

Chaotic Systems and Feedback

The definition of a chaotic system I use is any system in which a tiny change in initial conditions can lead to a large change in results.
Most chaotic systems are chaotic because they contain positive feedback. Positive feedback tends to amplify trends over time, while negative feedback tends to reduce trends over time. Complex systems such as climate and the financial markets have both positive and negative feedback.

In the weather, we can see positive feedback when a series of hot, sunny days create a static high pressure system which keeps storms from moving in to cool things off. When a storm does move in, you can get positive feedbacks cooling things off. National Weather Service forecaster Daryl Williams said the following about a storm which broke the summer heat wave in Oklahoma: “It’s kind of feeding on itself, cloud cover and rainfall cools the air and the ground.” (italics mine.)

In stock markets, financial bubbles grow with the help of several types of positive feedback. One such is “The specious association of money with intelligence,” as John Kenneth Galbraith described it in his short and very readable book on bubbles, A Short History of Financial Euphoria: Financial Genius is Before the Fall. When we see others make money in a stock market rise, we tend to think they must have been smart to have known when to get in. If we made money recently by buying stocks, we tend to think we are smart for having done so. In both cases, we’re more likely to think that buying stocks is a smart thing to do, even if the profits were just dumb luck. Collectively, this leads to more buying, which further raises prices. Even if those price rises are justified in the beginning, the positive feedback can carry them up far beyond any level justifiable by the value of the underlying companies. Many other positive feedbacks such as the wealth effect, relative valuation methods, and the increased ability to borrow against inflated asset prices operate in financial bubbles and bull markets. In contrast, fundamental and value investors produce negative feedbacks by buying when prices have fallen and selling when prices have risen.

As with weather, external shocks to the system can reverse even these self-reinforcing trends, as we recently saw when the US’s political paralysis around the debt ceiling debate and Europe’s inability to effectively deal with their debt crisis recently ended the two year bull market in July.

Strange Attractors and Regime Change

Highly complex systems which have both positive and negative feedbacks tend not to be chaotic all the time, but rather exhibit chaotic behavior only some of the time. The system will behave quite predictably in a deceptively regular fashion for a while, but then shift with little warning into another mode of behavior that is
also regular and predictable, but seems to follow a different set of rules.

Such behavior can be mapped with simple chaotic systems and often exhibits a pattern called a Strange Attractor, two of which are pictured with this article.
As the system moves through such a strange attractor, it will often stay in one set of the rings curves shown for an extended period, before jumping to another set after an unpredictable period.

In the weather, we see this sort of behavior with extended heat waves, cold spells, or periods when it is hot in the morning followed by an afternoon thunderstorm. Such patterns persist for days or weeks, but then quickly end to be replaced by a new pattern or a period of less predictable weather.

In the stock market, we have bull and bear markets. In bull markets, good news is greeted with euphoria and strong stock buying, while bad news is discounted or ignored. In bear markets, the opposite is true: good news is often ignored, while bad news leads to repeated bouts of selling. In his excellent but somewhat
inaccessible book, The Alchemy of Finance, George Soros describes how he tries to spot such tipping points or regime changes as they happen. Much theoretical work has been done to understand and model such changes, but the lesson I draw from chaos theory is that recognizing such changes in hindsight may be simple, but predicting them in advance is and will continue to be extremely difficult. That’s probably why Soros did a much better job describing market regimes than explaining how to spot them.

Nassim Taleb also addresses regime change in chaotic systems in his book The
Black Swan. His Black Swans are events which cannot be predicted solely by studying the past. Such events occur, he says, because the rules we infer from the observation of events never contain the full range of possibilities. He applies this lesson to societal events, personal experiences, and financial markets– all of which are chaotic systems. There are also climatic Black Swans.

Global Weirding

If you accept that the world’s climate is a chaotic system
characterized by a strange attractor and a large number of climate regimes such as ice ages and warm periods, you should also accept that the relatively small changes we are making to the atmosphere have the potential to shift the world’s climate into a new regime where the weather patterns humanity is familiar with are replaced with a new set of patterns that we’ve never seen before in human
history.

We are already aware of a few positive feedback mechanisms with the potential to amplify the effects of climate change, such as the ability of a release of methane from arctic permafrost and clathrates to rapidly accelerate global warming, or the disruption of the North Atlantic current due to melting polar glaciers. Such scenarios are chilling enough, but the knowledge that climate and weather are a chaotic system raises the possibility of yet unknown mechanisms that might create rapid climactic shifts. In a chaotic system, the past is not always a reliable guide to the future. Climactic past performance is
no guarantee of future climactic results.

“Global Warming” can sound somewhat comforting. “Climate Change” can sound clinical and distant. A better description is “Global Weirding:” the climate is not becoming a warmer version of what we’re used to, it’s becoming an entirely new system, with a new set of patterns that will surprise anyone expecting a version of the old climate regime.

Conclusion

There is only one climate, while there are hundreds if not thousands of financial markets operating at any one time. Financial markets also operate on a much more compressed time scale, with bubbles and busts compressed into a few short years or decades. Ice Ages, on the other hand, last tens of millions of years.

This difference financial markets and climate in number and scale means that we know much more about the chaos of financial markets than the chaos of climate. We’ve probably already seen most possible financial market regimes in at least one of the thousands of financial markets, from tulip bulbs to CDOs, that have operated
over the course of human history. Although the rules of markets change with new technology and communication, the basic rules of human psychology which govern these regimes have not. To paraphrase Mark Twain, financial history may not repeat itself, but it does rhyme.

Climactic history may also rhyme, but we’ve not yet read a full line of the poem: We don’t know what it will rhyme with. Ice ages and warm periods often last tens of millions of years. Given the infrequency of shifts between one climactic regime and another, it’s quite likely that the new climactic regime we are heading into will be unlike anything that has prevailed during human history, and possibly unlike anything in the geologic record.

The benefit of the slow pace of climactic history is that we do have a few years or decades during which we will be able to influence the path of global weirding.

In a chaotic system, a tiny change today can lead to a large change in future outcomes.

There’s a new paper from the Victoria Transportation Policy Institute looking into the price elasticity of both miles driven and fuel use. The author, Todd Litman, has done an in-depth literature survey which will be of interest to readers who liked my recent look into Jevons’ Paradox.

Jevons’ Paradox, also known as the rebound effect, states that increasing efficiency can lead to increased use of a resource, because the resource is now cheaper. I pointed out that this is only true in elastic markets, where the use of a resource is sensitive to price. In inelastic markets, it makes sense to mandate efficiency, because efficiency will not greatly increase use. In elastic markets, the best policy avenue is to increase the marginal price of usage.

One great example of this is the potential benefits of smart metering. While the early results of smart metering trials were very positive, seeming to show that average people would reduce their energy use by 10-15% when given good data, more recent and broader trials have shown that the actual effect is much smaller. The difference is that the early trials tended to be focused on particularly price-sensitive populations, such as people who had trouble paying their electricity bills who reduced their energy use for Woodstock Hydro. More recent trials have shown much lower reductions in bills because they have been serving the general populace, not just a particularly price sensitive subgroup, like the poor or people who volunteer to have smart meters installed.

1) While the price sensitivity of driving is quite elastic, the price sensitivity to fuel cost is much less elastic because fuel only accounts for about a quarter of the cost of driving.
2) Price sensitivities were temporarily depressed over the last 25 years due to various demographic changes, and now seem to be rebounding. As a result, many policies meant to reduce fuel use (such as higher CAFE standards) are likely to be less effective than expected due to the rebound effect. Better policies would work to increase the marginal cost of driving without increasing the total cost. Such policies include Pay as you drive car insurance and registration.

There’s much more. I highly recommend it or anyone interested in policies to reduce our dependence on foreign oil. Blurb follows:

There is growing interest in various transportation pricing reforms to help reduce traffic congestion, accidents, energy consumption and pollution emissions. Their effectiveness is affected by the price sensitivity of transport, that is, the degree that travelers respond to price changes, measured as elasticities (the percentage change in vehicle travel caused by a percentage change in price). Lower elasticities (price changes have relatively little impact on vehicle travel) imply that pricing reforms are not very effective at achieving objectives; that higher prices significantly harm consumers; and rebound effects (additional vehicle travel that results from increased fuel efficiency) are small so strategies such as fuel economy mandates are relatively effective at conserving fuel and reducing emissions. Higher elasticities imply that price reforms are relatively effective, consumers are able to reduce vehicle travel, and rebound effects are relatively large. Some studies found that price elasticities declined during the last quarter of the Twentieth Century, but recent evidence described indicates that transport is becoming more price sensitive. This report discusses the concepts of price elasticities and rebound effects, reviews information on vehicle travel and fuel price elasticities, examines evidence of changes in price elasticity values, and discusses policy implications.

Until I started reading Micheal Giberson’s posts on price gouging, I had not given the subject of price gouging much thought.

The main question of debate is “Is it moral for a retailer to charge more for a product when demand surges due to outside circumstances?” A classic example is charging for snow shovels in a snowstorm. In a recent post, Micheal poses the question:

I have to say that I don’t have a ready answer. I’m tempted to think that both store owners are acting morally, and that morality rests not with the store owner, but with the snow shovel customer.

If the customer plans ahead and receives the low “no snowstorm” price, there is no reason to complain. After all, who ever complains about a sale?

If the customer does not plan ahead, and is forced to buy the $20 shovel from the first store because the second store has run out, whose fault is it? I place the fault squarely on the customer who did not plan ahead for a snowstorm, and if that customer subsequently complains about price gouging, that complaint seems immoral in my eyes.

I think it’s everyone’s right to not plan for disaster if the consequences fall only on themselves. But if they then complain because they are being taken advantage of in the vulnerable position they’ve put themselves in, I have no sympathy.

Put simply, the store owners are planning for the snowstorm, and willing to accept the consequences of their actions. Buyers may or may not plan ahead, but it’s only when they are not willing to accept the consequences of their actions that I consider them immoral.

The only circumstance in which I’d place any moral onus on the store owner is when the disaster could not be foreseen. In this case, neither store owner will have snow shovels on hand because there will have been no market for snow shovels before the storm, so the whole question is moot anyway.

On the other hand, if the disaster can be be foreseen, but the consequences of not planning fall on society as a whole, then those who oppose preparing for the disaster are immoral because they are forcing others to share in the consequences of their decision.

If you read this blog regularly, you can probably figure out which coming disaster I have in mind. Are you advocating preparation, or opposing it?

Recent estimates of the long-run elasticity of driving are between -0.4 and -0.6, meaning that a 10% increase in the cost of driving should decrease miles driven by 4-6% over time.

There are several policy implications of rising elasticity:

1. People are more able to adjust their driving habits in response to changing prices, so pricing measures such as gas taxes, parking fees, and Pay-as-you-drive pricing are becoming more effective, and they are also more fair to the poor, who are likely to reducing driving more with an increase in price.

2. Vehicle efficiency standards will be less effective at cutting gasoline consumption due to the rebound effect: as the cost of driving drops with increased vehicle efficiency, people will drive more, partly offsetting the gasoline savings.

As a long-time listener to the Stephen Lacey’s weekly podcast, I was happy to join in as he takes an in-depth look at the Renewables Gap: the question of where the energy is going to come from to power the necessary transition to a clean energy economy, an issue I looked at in Managing the Peak Fossil Fuel Transition.

I’m in great company on this podcast, so if you don’t tune in for me, you might want to know what Bill McKibben has to say about it.